Predicting Stroke Risk Based on Health Behaviours: Development of the Stroke Population Risk Tool (SPoRT)

Background
Health behaviours, important factors in cardiovascular disease, are increasingly a focus of prevention. We appraised whether stroke risk can be accurately assessed using self-reported information focused on health behaviours.
Methods
Behavioural, sociodemographic and other risk factors were assessed in a population-based survey of 82 259 Ontarians who were followed for a median of 8.6 years (688 000 person-years follow-up) starting in 2001. Predictive algorithms for 5-year incident stroke resulting in hospitalization were created and then validated in a similar 2007 survey of 28 605 respondents (median 4.2 years follow-up).
Results
We observed 3 236 incident stroke events (1 551 resulting in hospitalization; 1 685 in the community setting without hospital admission). The final algorithms were discriminating (C-stat: 0.85, men; 0.87, women) and well-calibrated (in 65 of 67 subgroups for men; 61 of 65 for women). An index was developed to summarize cumulative relative risk of incident stroke from health behaviours and stress. For men, each point on the index corresponded to a 12% relative risk increase (180% risk difference, lowest (0) to highest (9) scores). For women, each point corresponded to a 14% relative risk increase (340% difference, lowest (0) to highest (11) scores). Algorithms for secondary stroke outcomes (stroke resulting in death; classified as ischemic; excluding transient ischemic attack; and in the community setting) had similar health behaviour risk hazards.
Conclusion
Incident stroke can be accurately predicted using self-reported information focused on health behaviours. Risk assessment can be performed with population health surveys to support population health planning or outside of clinical settings to support patient-focused prevention.

背景
健康行为作为心血管疾病的重要影响因素，其防控相关研究的关注度与日俱增。本研究旨在评估，仅依托聚焦健康行为的自我报告信息，能否准确评估卒中（stroke）风险。

方法
本研究依托2001年启动的一项基于人群的调查，纳入82259名安大略省居民，中位随访时长为8.6年（总随访人年数达688000），对受试者的行为学特征、社会人口学信息及其他卒中风险因素进行评估。研究构建了针对需住院治疗的5年新发卒中的预测算法，并在2007年一项纳入28605名受访者的同类调查队列中完成验证，该队列中位随访时长为4.2年。

结果
本研究共记录3236例新发卒中事件，其中1551例需住院治疗，1685例为社区发病且未住院的病例。最终构建的预测算法具备优异的区分效能（男性C统计量（C-statistic）为0.85，女性为0.87），且校准性能良好（男性67个亚组中65个符合校准标准，女性65个亚组中61个符合）。
研究开发了一项综合健康行为与压力相关的新发卒中累积相对风险指数。男性群体中，该指数每增加1分，相对风险升高12%（指数得分范围0~9，最低与最高得分间的风险差值达180%）；女性群体中，指数每增加1分，相对风险升高14%（指数得分范围0~11，最低与最高得分间的风险差值达340%）。
针对其他卒中相关结局（包括致死性卒中、缺血性卒中、排除短暂性脑缺血发作（transient ischemic attack, TIA）病例以及社区发病卒中）的预测算法，其健康行为风险关联效应与前述结果保持一致。

结论
仅依托聚焦健康行为的自我报告信息，即可准确预测首发卒中风险。该风险评估可通过人群健康调查开展，用于辅助人群健康规划；也可在临床场景之外实施，助力以患者为中心的卒中防控工作。